# TODO: 导入必要的库和模块
import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import load_digits
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score
from tqdm import tqdm
import pickle

# TODO: 加载数字数据集
digits = load_digits()
X = digits.data  # 特征数据（图像像素）
y = digits.target  # 标签（对应的数字）

# TODO: 将数据集划分为训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42  # 固定随机种子保证结果可复现
)

# TODO: 初始化变量以存储最佳准确率，相应的k值和最佳knn模型
best_accuracy = 0.0
best_k = 1
best_knn = None

# TODO: 初始化一个列表以存储每个k值的准确率
k_range = range(1, 41)
accuracies = []

# TODO: 尝试从1到40的k值，对于每个k值，训练knn模型，保存最佳准确率，k值和knn模型
print("正在计算不同K值的准确率...")
for k in tqdm(k_range, desc="训练进度"):
    # 训练KNN模型
    knn = KNeighborsClassifier(n_neighbors=k)
    knn.fit(X_train, y_train)
    
    # 计算准确率
    y_pred = knn.predict(X_test)
    accuracy = accuracy_score(y_test, y_pred)
    accuracies.append(accuracy)
    
    # 更新最佳模型
    if accuracy > best_accuracy:
        best_accuracy = accuracy
        best_k = k
        best_knn = knn

# TODO: 将最佳KNN模型保存到二进制文件
with open('best_knn_model.pkl', 'wb') as f:
    pickle.dump(best_knn, f)

# 绘制并保存准确率变化图
plt.figure(figsize=(10, 6))
plt.plot(k_range, accuracies, marker='o', color='b')
plt.xlabel('K值')
plt.ylabel('准确率')
plt.title('不同K值下KNN模型的准确率')
plt.xticks(k_range)
plt.grid(linestyle='--', alpha=0.7)

# 标记最佳K值
plt.axvline(x=best_k, color='r', linestyle='--')
plt.text(
    best_k + 0.5, best_accuracy,
    f'K={best_k}, 准确率={best_accuracy:.4f}',
    color='r', fontweight='bold'
)

plt.tight_layout()
plt.savefig('accuracy_plot.pdf')
plt.close()

# TODO: 打印最佳准确率和相应的k值
print(f"\n最优K值: {best_k}")
print(f"最优准确率: {best_accuracy:.4f} ({best_accuracy*100:.2f}%)")
print("模型已保存为 'best_knn_model.pkl'")
print("准确率变化图已保存为 'accuracy_plot.pdf'")